{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LAB 1a:  Exploring natality dataset.\n",
    "\n",
    "**Learning Objectives**\n",
    "\n",
    "1. Use BigQuery to explore natality dataset\n",
    "1. Use Cloud AI Platform Notebooks to plot data explorations\n",
    "\n",
    "\n",
    "## Introduction \n",
    "In this notebook, we will explore the natality dataset before we begin model development and training to predict the weight of a baby before it is born.  We will use BigQuery to explore the data and use Cloud AI Platform Notebooks to plot data explorations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hJ7ByvoXzpVI"
   },
   "source": [
    "## Load necessary libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "mC9K9Dpx1ztf"
   },
   "source": [
    "Check that the Google BigQuery library is installed and if not, install it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "google-cloud-bigquery==1.6.1\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "sudo pip3 freeze | grep google-cloud-bigquery==1.6.1 || \\\n",
    "sudo pip3 install google-cloud-bigquery==1.6.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import bigquery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L0-vOB4y2BJM"
   },
   "source": [
    "## The source dataset\n",
    "\n",
    "Our dataset is hosted in [BigQuery](https://cloud.google.com/bigquery/). The CDC's Natality data has details on US births from 1969 to 2008 and is a publically available dataset, meaning anyone with a GCP account has access. Click [here](https://console.cloud.google.com/bigquery?project=bigquery-public-data&p=publicdata&d=samples&t=natality&page=table) to access the dataset.\n",
    "\n",
    "The natality dataset is relatively large at almost 138 million rows and 31 columns, but simple to understand. `weight_pounds` is the target, the continuous value we’ll train a model to predict."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<h2> Explore data </h2>\n",
    "\n",
    "The data is natality data (record of births in the US). The goal is to predict the baby's weight given a number of factors about the pregnancy and the baby's mother.  Later, we will want to split the data into training and eval datasets. The hash of the year-month will be used for that -- this way, twins born on the same day won't end up in different cuts of the data. We'll first create a SQL query using the natality data after the year 2000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "    weight_pounds,\n",
    "    is_male,\n",
    "    mother_age,\n",
    "    plurality,\n",
    "    gestation_weeks,\n",
    "    FARM_FINGERPRINT(\n",
    "        CONCAT(\n",
    "            CAST(YEAR AS STRING),\n",
    "            CAST(month AS STRING)\n",
    "        )\n",
    "    ) AS hashmonth\n",
    "FROM\n",
    "    publicdata.samples.natality\n",
    "WHERE\n",
    "    year > 2000\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a BigQuery client that we can use throughout the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bq = bigquery.Client()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now examine the result of a BiqQuery call in a Pandas DataFrame using our newly created client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight_pounds</th>\n",
       "      <th>is_male</th>\n",
       "      <th>mother_age</th>\n",
       "      <th>plurality</th>\n",
       "      <th>gestation_weeks</th>\n",
       "      <th>hashmonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.818490</td>\n",
       "      <td>False</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>1403073183891835564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.141671</td>\n",
       "      <td>False</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>8904940584331855459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.948072</td>\n",
       "      <td>True</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>7108882242435606404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.838332</td>\n",
       "      <td>True</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>3408502330831153141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.259415</td>\n",
       "      <td>True</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1088037545023002395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight_pounds  is_male  mother_age  plurality  gestation_weeks  \\\n",
       "0       8.818490    False          17          1               42   \n",
       "1       8.141671    False          29          1               38   \n",
       "2       5.948072     True          38          1               38   \n",
       "3       8.838332     True          27          1               39   \n",
       "4       9.259415     True          28          1               38   \n",
       "\n",
       "             hashmonth  \n",
       "0  1403073183891835564  \n",
       "1  8904940584331855459  \n",
       "2  7108882242435606404  \n",
       "3  3408502330831153141  \n",
       "4  1088037545023002395  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = bq.query(query + \" LIMIT 100\").to_dataframe()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's get the set of all valid column names in the natality dataset. We can do this by accessing the `INFORMATION_SCHEMA` for the table from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Query to get all column names within table schema\n",
    "sql = \"\"\"\n",
    "SELECT\n",
    "    column_name\n",
    "FROM\n",
    "    publicdata.samples.INFORMATION_SCHEMA.COLUMNS\n",
    "WHERE\n",
    "    table_name = \"natality\"\n",
    "\"\"\"\n",
    "\n",
    "# Send query through BigQuery client and store output to a dataframe\n",
    "valid_columns_df = bq.query(sql).to_dataframe()\n",
    "\n",
    "# Convert column names in dataframe to a set\n",
    "valid_columns_set = valid_columns_df[\"column_name\"].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can print our valid columns set to see all of the possible columns we have available in the dataset. Of course, you could also find this information by going to the `Schema` tab when selecting the table in the [BigQuery UI](https://console.cloud.google.com/bigquery?project=bigquery-public-data&p=publicdata&d=samples&t=natality&page=table)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['source_year', 'year', 'month', 'day', 'wday', 'state', 'is_male', 'child_race', 'weight_pounds', 'plurality', 'apgar_1min', 'apgar_5min', 'mother_residence_state', 'mother_race', 'mother_age', 'gestation_weeks', 'lmp', 'mother_married', 'mother_birth_state', 'cigarette_use', 'cigarettes_per_day', 'alcohol_use', 'drinks_per_week', 'weight_gain_pounds', 'born_alive_alive', 'born_alive_dead', 'born_dead', 'ever_born', 'father_race', 'father_age', 'record_weight']\n"
     ]
    }
   ],
   "source": [
    "print(valid_columns_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Let's write a query to find the unique values for each of the columns and the count of those values.\n",
    "This is important to ensure that we have enough examples of each data value, and to verify our hunch that the parameter has predictive value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def get_distinct_values(valid_columns_set, column_name):\n",
    "    \"\"\"Gets distinct value statistics of BigQuery data column.\n",
    "\n",
    "    Args:\n",
    "        valid_columns_set: set, the set of all possible valid column names in\n",
    "            table.\n",
    "        column_name: str, name of column in BigQuery.\n",
    "    Returns:\n",
    "        Dataframe of unique values, their counts, and averages.\n",
    "    \"\"\"\n",
    "    assert column_name in valid_columns_set, (\n",
    "        \"{column_name} is not a valid column_name\".format(\n",
    "            column_name=column_name))\n",
    "\n",
    "    sql = \"\"\"\n",
    "    SELECT\n",
    "        {column_name},\n",
    "        COUNT(1) AS num_babies,\n",
    "        AVG(weight_pounds) AS avg_wt\n",
    "    FROM\n",
    "        publicdata.samples.natality\n",
    "    WHERE\n",
    "        year > 2000\n",
    "    GROUP BY\n",
    "        {column_name}\n",
    "    \"\"\".format(column_name=column_name)\n",
    "\n",
    "    return bq.query(sql).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_distinct_values(valid_columns_set, column_name, logy=False):\n",
    "    \"\"\"Plots distinct value statistics of BigQuery data column.\n",
    "\n",
    "    Args:\n",
    "        valid_columns_set: set, the set of all possible valid column names in\n",
    "            table.\n",
    "        column_name: str, name of column in BigQuery.\n",
    "        logy: bool, if plotting counts in log scale or not.\n",
    "    \"\"\"\n",
    "    df = get_distinct_values(valid_columns_set, column_name)\n",
    "    df = df.sort_values(column_name)\n",
    "    df.plot(\n",
    "        x=column_name, y=\"num_babies\", logy=logy, kind=\"bar\", figsize=(12, 5))\n",
    "    df.plot(x=column_name, y=\"avg_wt\", kind=\"bar\", figsize=(12, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a bar plot to see `is_male` with `avg_wt` linearly scaled and `num_babies` logarithmically scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distinct_values(valid_columns_set, column_name=\"is_male\", logy=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a bar plot to see `mother_age` with `avg_wt` linearly scaled and `num_babies` linearly scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distinct_values(valid_columns_set, column_name=\"mother_age\", logy=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a bar plot to see `plurality` with `avg_wt` linearly scaled and `num_babies` logarithmically scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArwAAAE9CAYAAADpvrGjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAFARJREFUeJzt3X+w3XWd3/HXG4ibRSgGDM7OZt2LDIPgj/LjwioExtVqQTDIlE5Dp7vKbJs/ql0629JJddpOx2mLi9OuTp21GcuyXRV/4IKKVlddqQjiGhS2K+DMqiBxXIm0FVCzgr77Ry5uiJF7kpyTc/Ph8ZjJ5Pz4nu9938yB+8w3n/P9VncHAABGdci8BwAAgFkSvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADO2wWez0mc98Zi8sLMxi1wAAkCS5/fbbv9vda5fbbibBu7CwkK1bt85i1wAAkCSpqvsm2c6SBgAAhiZ4AQAYmuAFAGBoM1nDCwDA3nv00Uezbdu27NixY96jrCirV6/OunXrsmrVqn16veAFAFghtm3bliOPPDILCwupqnmPsyJ0dx588MFs27Ytxx133D7tw5IGAIAVYseOHTnmmGPE7i6qKsccc8x+HfUWvAAAK4jY/Vn7+2cieAEAmLsbbrghd91110z2bQ0vAMAKtbD5o1Pd371XXjDV/U3TDTfckAsvvDAnn3zy1PftCC8AAD/16le/Oqeffnqe97znZcuWLXnHO96RK6644qfPX3PNNXn961+fJHnTm96UE088MevXr8+ll16at7zlLXvc5wMPPJDTTz89SXLnnXemqvLNb34zSXL88cfn1ltvzYc//OFcccUVOeWUU/K1r31tqt+TI7wAAPzU1VdfnaOPPjo//OEPc8YZZ+TTn/50zj777Fx11VVJkve973154xvfmC9+8Yv54Ac/mDvvvDOPPvpoTjvttJ9G7e6OPfbY7NixIw899FBuvvnmLC4u5uabb8769etz7LHH5qyzzsqGDRty4YUX5pJLLpn69zRs8E77nwAOZiv5ny8AgJXlbW97W66//vokyf33359vfOMbec5znpPbbrstJ5xwQu65556cffbZeetb35qLLrooq1evzurVq/OqV73qSfd71lln5ZZbbslnP/vZvOENb8jHP/7xdHfOOeecmX9PwwYvAAB756abbsqnPvWpfP7zn8/hhx+el7zkJdmxY0c2btyY97///Xnuc5+biy++eJ/OmnDuuefm5ptvzn333ZeLLroob37zm1NVueCC2R+Ys4YXAIAkyfe+972sWbMmhx9+eO65557cdtttSZKLL744H/rQh3Lttddm48aNSZKzzz47H/nIR7Jjx4488sgjufHGG5903+ecc07e9a535YQTTsghhxySo48+Oh/72Meyfv36JMmRRx6Zhx9+eCbfl+AFACBJct555+Wxxx7LSSedlM2bN+dFL3pRkmTNmjU56aSTct999+XMM89MkpxxxhnZsGFDXvjCF+b888/PC17wghx11FE/d98LCwvp7px77rlJkvXr1+cZz3hG1qxZkyTZuHFjrrrqqpx66qlT/9BadfdUd5gki4uLvXXr1qnvd29Yw/s3rOEFgIPD3XffnZNOOmneY0zskUceyRFHHJEf/OAHOffcc7Nly5acdtppM/lae/qzqarbu3txuddawwsAwD7ZtGlT7rrrruzYsSOvec1rZha7+2vZ4K2qE5O8b5eHnpPk33b3781sKgAAVrz3vOc9P/PY6173utxyyy1PeOzyyy/PZZdddqDG+hnLBm93fzXJKUlSVYcm+VaS62c8FwAAB6G3v/3t8x7hZ+zth9ZeluRr3X3fLIYBAIBp29vg3Zjk2lkMAgBAMosTChzs9vfPZOLgraqnJdmQ5AM/5/lNVbW1qrZu3759v4YCAHgqWr16dR588EHRu4vuzoMPPpjVq1fv8z725iwN5yf5Und/5+cMsyXJlmTnacn2eSIAgKeodevWZdu2bXHw8IlWr16ddevW7fPr9yZ4L43lDAAAM7Nq1aocd9xx8x5jOBMtaaiqpyd5eZI/nu04AAAwXRMd4e3u7yc5ZsazAADA1O3tWRoAAOCgIngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGgTBW9VPaOqrquqe6rq7qp68awHAwCAaThswu3emuTj3X1JVT0tyeEznAkAAKZm2eCtqqOSnJvktUnS3T9K8qPZjgUAANMxyZKG45JsT/IHVfXlqnpnVT19xnMBAMBUTBK8hyU5Lcnvd/epSb6fZPPuG1XVpqraWlVbt2/fPuUxAQBg30wSvNuSbOvuLyzdvy47A/gJuntLdy929+LatWunOSMAAOyzZYO3u/8qyf1VdeLSQy9LctdMpwIAgCmZ9CwN/yzJu5fO0PD1JJfNbiQAAJieiYK3u+9IsjjjWQAAYOpcaQ0AgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEdNu8B4EBa2PzReY+wYtx75QXzHgEADoiJgreq7k3ycJIfJ3msuxdnORQAAEzL3hzh/fXu/u7MJgEAgBmwhhcAgKFNGryd5E+q6vaq2jTLgQAAYJomXdKwvru/VVXHJvlkVd3T3Z/ddYOlEN6UJM9+9rOnPCYAAOybiY7wdve3ln5/IMn1Sc7cwzZbunuxuxfXrl073SkBAGAfLRu8VfX0qjry8dtJXpHkL2Y9GAAATMMkSxqeleT6qnp8+/d098dnOhUAAEzJssHb3V9P8rcPwCwAADB1TksGAMDQBC8AAEMTvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADG3i4K2qQ6vqy1V14ywHAgCAadqbI7yXJ7l7VoMAAMAsTBS8VbUuyQVJ3jnbcQAAYLomPcL7e0n+VZKfzHAWAACYumWDt6ouTPJAd9++zHabqmprVW3dvn371AYEAID9MckR3rOTbKiqe5O8N8lLq+pdu2/U3Vu6e7G7F9euXTvlMQEAYN8sG7zd/a+7e113LyTZmORPu/sfzXwyAACYAufhBQBgaIftzcbdfVOSm2YyCQAAzIAjvAAADE3wAgAwNMELAMDQBC8AAEMTvAAADE3wAgAwtL06LRnAiBY2f3TeI6wY9155wbxHAJg6R3gBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoQleAACGJngBABia4AUAYGiCFwCAoS0bvFW1uqr+rKrurKqvVNW/PxCDAQDANBw2wTZ/neSl3f1IVa1K8rmq+p/dfduMZwMAgP22bPB2dyd5ZOnuqqVfPcuhAABgWiZaw1tVh1bVHUkeSPLJ7v7CbMcCAIDpmCh4u/vH3X1KknVJzqyq5+++TVVtqqqtVbV1+/bt054TAAD2yV6dpaG7/1+SzyQ5bw/Pbenuxe5eXLt27bTmAwCA/TLJWRrWVtUzlm7/YpKXJ7ln1oMBAMA0THKWhl9K8odVdWh2BvL7u/vG2Y4FAADTMclZGv48yakHYBYAAJi6SY7wAsBTzsLmj857hBXj3isvmPcIsF9cWhgAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAY2mHzHgAA4GCxsPmj8x5hxbj3ygvmPcLEHOEFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEJXgAAhiZ4AQAYmuAFAGBoghcAgKEtG7xV9StV9ZmququqvlJVlx+IwQAAYBoOm2Cbx5L8i+7+UlUdmeT2qvpkd98149kAAGC/LXuEt7u/3d1fWrr9cJK7k/zyrAcDAIBp2Ks1vFW1kOTUJF+YxTAAADBtEwdvVR2R5INJ/nl3P7SH5zdV1daq2rp9+/ZpzggAAPtsouCtqlXZGbvv7u4/3tM23b2luxe7e3Ht2rXTnBEAAPbZJGdpqCT/Pcnd3f2fZz8SAABMzyRHeM9O8htJXlpVdyz9euWM5wIAgKlY9rRk3f25JHUAZgEAgKlzpTUAAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGNqywVtVV1fVA1X1FwdiIAAAmKZJjvBek+S8Gc8BAAAzsWzwdvdnk/yfAzALAABMnTW8AAAMbWrBW1WbqmprVW3dvn37tHYLAAD7ZWrB291bunuxuxfXrl07rd0CAMB+saQBAIChTXJasmuTfD7JiVW1rap+a/ZjAQDAdBy23AbdfemBGAQAAGbBkgYAAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAIChCV4AAIYmeAEAGNpEwVtV51XVV6vqL6tq86yHAgCAaVk2eKvq0CRvT3J+kpOTXFpVJ896MAAAmIZJjvCemeQvu/vr3f2jJO9NctFsxwIAgOmYJHh/Ocn9u9zftvQYAACseNXdT75B1SVJzuvuf7x0/zeS/Fp3v3637TYl2bR098QkX53+uAelZyb57ryHYEXxnmBPvC/YE+8L9sT74m/8anevXW6jwybY0beS/Mou99ctPfYE3b0lyZaJx3uKqKqt3b047zlYObwn2BPvC/bE+4I98b7Ye5MsafhikhOq6riqelqSjUk+PNuxAABgOpY9wtvdj1XV65N8IsmhSa7u7q/MfDIAAJiCSZY0pLs/luRjM55lVJZ5sDvvCfbE+4I98b5gT7wv9tKyH1oDAICDmUsLAwAwNMELAMDQBC8AAEMTvDBjVfXcqnpZVR2x2+PnzWsm5q+qzqyqM5Zun1xVv1NVr5z3XKwsVfU/5j0DK0tVrV/6/8Ur5j3LwcSH1g6Qqrqsu/9g3nNwYFXVbyd5XZK7k5yS5PLu/tDSc1/q7tPmOR/zUVX/Lsn52XmmnE8m+bUkn0ny8iSf6O7/MMfxmJOq2v0c95Xk15P8aZJ094YDPhRzV1V/1t1nLt3+J9n5M+X6JK9I8pHuvnKe8x0sBO8BUlXf7O5nz3sODqyq+t9JXtzdj1TVQpLrkvxRd7+1qr7c3afOdUDmYul9cUqSX0jyV0nWdfdDVfWLSb7Q3S+c64DMRVV9KcldSd6ZpLMzeK/Nzgs+pbv/1/ymY152/VlRVV9M8sru3l5VT09yW3e/YL4THhwmOg8vk6mqP/95TyV51oGchRXjkO5+JEm6+96qekmS66rqV7PzfcFT02Pd/eMkP6iqr3X3Q0nS3T+sqp/MeTbmZzHJ5UnemOSK7r6jqn4odJ/yDqmqNdm5DLW6e3uSdPf3q+qx+Y528BC80/WsJH83yf/d7fFKcuuBH4cV4DtVdUp335EkS0d6L0xydRJ/K3/q+lFVHd7dP0hy+uMPVtVRSQTvU1R3/yTJf6mqDyz9/p34OU1yVJLbs7Mluqp+qbu/vfS5EAdOJuQ/pOm6MckRj8fNrqrqpgM/DivAbyZ5wt/Au/uxJL9ZVf9tPiOxApzb3X+d/DRyHrcqyWvmMxIrRXdvS/L3q+qCJA/Nex7mq7sXfs5TP0ly8QEc5aBmDS8AAENzWjIAAIYmeAEAGJrgBZiyqrqpqhantK+XVNWNS7c3VNXmpduvrqqTp/E1AEYneAHmrKom+gBxd394l5PMvzqJ4AWYgOAF2EdVtVBV91TVu6vq7qq6rqoO322bR3a5fUlVXbN0+5qqekdVfSHJ7y5davjzVfXlqrq1qk7cw9d7bVX916o6K8mGJFdV1R1VdfzSRQse3+6EXe8DPNU5LRnA/jkxyW919y1VdXWSf7oXr12X5Kzu/nFV/a0k53T3Y1X1d5L8xyR/b08v6u5bly5De2N3X5ckVfW9Xc75fFkSlzIHWCJ4AfbP/d19y9LtdyX57b147QeWrriW7Dy5/B9W1QnZeVnZVXs5xzuTXFZVv5PkHyQ5cy9fDzAsSxoA9s/uJzN/svurd3vu+7vcflOSz3T385O8ag/bLueDSc5PcmGS27v7wb18PcCwBC/A/nl2Vb146fY/TPK53Z7/TlWdVFWH5MmvinRUkm8t3X7tBF/34SRHPn6nu3ck+USS34/lDABPIHgB9s9Xk7yuqu5OsiY7g3NXm7PzsuO3Jvn2k+znd5P8p6r6ciZbbvbeJFcsfcjt+KXH3p2dlxv9k72YH2B4Li0MsI+qaiE7Pzj2/DmPkiSpqn+Z5Kju/jfzngVgJfGhNYABVNX1SY5P8tJ5zwKw0jjCCwDA0KzhBQBgaIIXAIChCV4AAIYmeAEAGJrgBQBgaIIXAICh/X8rLUdWRDQkfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distinct_values(valid_columns_set, column_name=\"plurality\", logy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a bar plot to see `gestation_weeks` with `avg_wt` linearly scaled and `num_babies` logarithmically scaled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_distinct_values(\n",
    "    valid_columns_set, column_name=\"gestation_weeks\", logy=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "All these factors seem to play a part in the baby's weight. Male babies are heavier on average than female babies. Teenaged and older moms tend to have lower-weight babies. Twins, triplets, etc. are lower weight than single births. Preemies weigh in lower as do babies born to single moms. In addition, it is important to check whether you have enough data (number of babies) for each input value. Otherwise, the model prediction against input values that doesn't have enough data may not be reliable.\n",
    "<p>\n",
    "In the next notebooks, we will develop a machine learning model to combine all of these factors to come up with a prediction of a baby's weight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lab Summary: \n",
    "In this lab, we used BigQuery to explore the data and used Cloud AI Platform Notebooks to plot data explorations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Copyright 2019 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
   ]
  }
 ],
 "metadata": {
  "environment": {
   "name": "tf2-gpu.2-1.m65",
   "type": "gcloud",
   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-1:m65"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
